boolean association rules

基本解釋

Discovering association rules is an important data mining problem. While discovering Quantitative Association Rules differs from traditional Boolean Association Rules.
關(guān)聯(lián)規(guī)則的發(fā)現(xiàn)是數(shù)據(jù)挖掘的一個重要方面，而數(shù)量關(guān)聯(lián)規(guī)則的發(fā)現(xiàn)不同于傳統(tǒng)的布爾型關(guān)聯(lián)規(guī)則。
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Most quantitative association rules transform mining association rules of numeric property into boolean property, and the kernel problem is to divide the numeric data into intervals.
數(shù)值型關(guān)聯(lián)規(guī)則的算法大多是將多值屬性關(guān)聯(lián)規(guī)則挖掘問題轉(zhuǎn)化為布爾型關(guān)聯(lián)規(guī)則挖掘問題，而連續(xù)屬性的離散化是數(shù)值型關(guān)聯(lián)規(guī)則的核心問題。
In Chapter 3, the effective Adaptive-Support Boolean algorithm for mining association rules is presented, including the Adaptive-Support frame of the algorithm and its Boolean fundamentals.
第三章提出了一種基于自適應(yīng)支持度的布爾式關(guān)聯(lián)規(guī)則挖掘算法，包括布爾式挖掘原理及挖掘算法的自適應(yīng)支持度框架。

布爾關(guān)聯(lián)規(guī)則
It is proved that every WRR can induce a Boolean association rules as its support rule.4.
證明了任意一個弱比例規(guī)則都可誘導(dǎo)出一個布爾關(guān)聯(lián)規(guī)則作為其支撐規(guī)則。